CBTI Summary

Consort map

Demographic information

Characteristic

N

Overall, N = 3581

control, N = 1791

treatment, N = 1791

p-value2

age

358

36.34 ± 13.94 (18 - 73)

35.95 ± 13.84 (18 - 73)

36.72 ± 14.07 (18 - 71)

0.599

gender

358

0.792

female

286 (80%)

142 (79%)

144 (80%)

male

72 (20%)

37 (21%)

35 (20%)

occupation

358

0.658

civil

13 (3.6%)

4 (2.2%)

9 (5.0%)

clerk

57 (16%)

30 (17%)

27 (15%)

craft

12 (3.4%)

8 (4.5%)

4 (2.2%)

homemaker

26 (7.3%)

14 (7.8%)

12 (6.7%)

manager

28 (7.8%)

16 (8.9%)

12 (6.7%)

other

15 (4.2%)

5 (2.8%)

10 (5.6%)

professional

39 (11%)

16 (8.9%)

23 (13%)

retired

21 (5.9%)

10 (5.6%)

11 (6.1%)

service

12 (3.4%)

7 (3.9%)

5 (2.8%)

student

119 (33%)

60 (34%)

59 (33%)

unemploy

16 (4.5%)

9 (5.0%)

7 (3.9%)

marital

358

0.652

divorced

14 (3.9%)

5 (2.8%)

9 (5.0%)

married

97 (27%)

51 (28%)

46 (26%)

other

2 (0.6%)

1 (0.6%)

1 (0.6%)

separated

5 (1.4%)

1 (0.6%)

4 (2.2%)

single

235 (66%)

119 (66%)

116 (65%)

widowed

5 (1.4%)

2 (1.1%)

3 (1.7%)

marital_r

358

0.252

married

97 (27%)

51 (28%)

46 (26%)

other

26 (7.3%)

9 (5.0%)

17 (9.5%)

single

235 (66%)

119 (66%)

116 (65%)

education

358

0.914

post-secondary

52 (15%)

28 (16%)

24 (13%)

primary

2 (0.6%)

1 (0.6%)

1 (0.6%)

secondary

50 (14%)

24 (13%)

26 (15%)

university

254 (71%)

126 (70%)

128 (72%)

education_r

358

0.819

post-secondary

52 (15%)

28 (16%)

24 (13%)

secondary or below

52 (15%)

25 (14%)

27 (15%)

university

254 (71%)

126 (70%)

128 (72%)

family_income

358

0.502

0_10000

56 (16%)

27 (15%)

29 (16%)

10001_20000

75 (21%)

38 (21%)

37 (21%)

20001_30000

73 (20%)

42 (23%)

31 (17%)

30001_40000

60 (17%)

31 (17%)

29 (16%)

40000_above

94 (26%)

41 (23%)

53 (30%)

religion

358

0.110

buddhism

16 (4.5%)

7 (3.9%)

9 (5.0%)

catholic

17 (4.7%)

11 (6.1%)

6 (3.4%)

christianity

73 (20%)

30 (17%)

43 (24%)

nil

248 (69%)

130 (73%)

118 (66%)

other

3 (0.8%)

0 (0%)

3 (1.7%)

taoism

1 (0.3%)

1 (0.6%)

0 (0%)

religion_r

358

0.234

buddhism

16 (4.5%)

7 (3.9%)

9 (5.0%)

catholic

17 (4.7%)

11 (6.1%)

6 (3.4%)

christianity

73 (20%)

30 (17%)

43 (24%)

nil

248 (69%)

130 (73%)

118 (66%)

other

4 (1.1%)

1 (0.6%)

3 (1.7%)

source

358

0.233

bokss

15 (4.2%)

11 (6.1%)

4 (2.2%)

facebook

131 (37%)

63 (35%)

68 (38%)

instagram

12 (3.4%)

7 (3.9%)

5 (2.8%)

other

66 (18%)

28 (16%)

38 (21%)

refresh

134 (37%)

70 (39%)

64 (36%)

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Table

Characteristic

N

Overall, N = 3581

control, N = 1791

treatment, N = 1791

p-value2

isi

358

13.47 ± 3.37 (8 - 21)

13.53 ± 3.33 (8 - 21)

13.40 ± 3.42 (8 - 21)

0.719

who

358

39.61 ± 14.94 (0 - 84)

39.28 ± 14.84 (4 - 80)

39.93 ± 15.08 (0 - 84)

0.682

phq

358

8.51 ± 5.01 (0 - 25)

8.21 ± 4.98 (0 - 21)

8.80 ± 5.03 (0 - 25)

0.264

gad

358

7.78 ± 5.12 (0 - 21)

7.54 ± 5.03 (0 - 21)

8.02 ± 5.21 (0 - 21)

0.376

wsas

358

16.73 ± 9.85 (0 - 40)

16.77 ± 9.70 (0 - 39)

16.69 ± 10.03 (0 - 40)

0.936

shps_arousal

358

3.10 ± 0.69 (1 - 5)

3.02 ± 0.68 (1 - 5)

3.18 ± 0.69 (1 - 5)

0.025

shps_schedule

358

3.55 ± 0.87 (1 - 6)

3.53 ± 0.81 (2 - 6)

3.58 ± 0.93 (1 - 6)

0.653

shps_behavior

358

2.05 ± 0.66 (1 - 4)

1.99 ± 0.61 (1 - 4)

2.12 ± 0.71 (1 - 4)

0.059

shps_environment

358

2.30 ± 0.82 (1 - 5)

2.33 ± 0.84 (1 - 5)

2.27 ± 0.80 (1 - 5)

0.473

dbas_consequence

358

6.61 ± 1.75 (1 - 10)

6.59 ± 1.82 (1 - 10)

6.64 ± 1.68 (1 - 10)

0.772

dbas_worry

358

14.37 ± 3.23 (3 - 20)

14.20 ± 3.35 (3 - 20)

14.54 ± 3.11 (3 - 20)

0.319

dbas_expectation

358

7.03 ± 2.14 (1 - 10)

7.17 ± 2.09 (1 - 10)

6.89 ± 2.19 (1 - 10)

0.209

dbas_medication

358

3.19 ± 2.07 (0 - 9)

3.15 ± 2.04 (0 - 9)

3.24 ± 2.09 (0 - 9)

0.683

psas_somatic

358

1.88 ± 0.69 (1 - 5)

1.86 ± 0.66 (1 - 4)

1.91 ± 0.71 (1 - 5)

0.539

psas_cognitive

358

2.92 ± 0.85 (1 - 5)

2.87 ± 0.84 (1 - 5)

2.97 ± 0.86 (1 - 5)

0.270

psqi_global

358

10.36 ± 3.08 (2 - 19)

10.14 ± 3.13 (4 - 17)

10.58 ± 3.03 (2 - 19)

0.176

mic_attention

358

1.36 ± 0.72 (0 - 3)

1.30 ± 0.71 (0 - 3)

1.42 ± 0.73 (0 - 3)

0.110

mic_executive

358

1.31 ± 0.76 (0 - 3)

1.28 ± 0.77 (0 - 3)

1.35 ± 0.76 (0 - 3)

0.406

mic_memory

358

1.37 ± 0.73 (0 - 3)

1.33 ± 0.75 (0 - 3)

1.40 ± 0.71 (0 - 3)

0.397

nb_pcs

358

46.27 ± 8.63 (17 - 65)

46.33 ± 8.91 (17 - 63)

46.20 ± 8.38 (21 - 65)

0.879

nb_mcs

358

39.94 ± 9.95 (8 - 65)

39.90 ± 9.78 (8 - 62)

39.98 ± 10.14 (8 - 65)

0.935

1Mean ± SD (Range)

2Two Sample t-test

Plot

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

isi

(Intercept)

13.5

0.286

13.0, 14.1

group

control

—

—

—

treatment

-0.128

0.404

-0.921, 0.664

0.751

time_point

1st

—

—

—

2nd

-2.46

0.322

-3.09, -1.83

0.000

3rd

-2.87

0.329

-3.52, -2.23

0.000

group * time_point

treatment * 2nd

-2.96

0.486

-3.91, -2.01

0.000

treatment * 3rd

-2.96

0.494

-3.93, -2.00

0.000

Pseudo R square

0.259

who

(Intercept)

39.3

1.220

36.9, 41.7

group

control

—

—

—

treatment

0.648

1.726

-2.73, 4.03

0.707

time_point

1st

—

—

—

2nd

2.92

1.194

0.578, 5.26

0.015

3rd

3.74

1.221

1.35, 6.13

0.002

group * time_point

treatment * 2nd

5.61

1.808

2.06, 9.15

0.002

treatment * 3rd

6.49

1.841

2.88, 10.1

0.000

Pseudo R square

0.053

phq

(Intercept)

8.21

0.378

7.47, 8.95

group

control

—

—

—

treatment

0.592

0.535

-0.456, 1.64

0.269

time_point

1st

—

—

—

2nd

-0.779

0.333

-1.43, -0.127

0.020

3rd

-0.658

0.341

-1.33, 0.009

0.054

group * time_point

treatment * 2nd

-1.73

0.506

-2.73, -0.743

0.001

treatment * 3rd

-2.43

0.515

-3.43, -1.42

0.000

Pseudo R square

0.039

gad

(Intercept)

7.54

0.381

6.79, 8.28

group

control

—

—

—

treatment

0.480

0.539

-0.577, 1.54

0.373

time_point

1st

—

—

—

2nd

-0.440

0.341

-1.11, 0.228

0.197

3rd

-0.561

0.348

-1.24, 0.122

0.108

group * time_point

treatment * 2nd

-2.07

0.517

-3.09, -1.06

0.000

treatment * 3rd

-2.40

0.527

-3.43, -1.37

0.000

Pseudo R square

0.038

wsas

(Intercept)

16.8

0.748

15.3, 18.2

group

control

—

—

—

treatment

-0.084

1.058

-2.16, 1.99

0.937

time_point

1st

—

—

—

2nd

-0.819

0.694

-2.18, 0.541

0.238

3rd

-0.156

0.710

-1.55, 1.24

0.826

group * time_point

treatment * 2nd

-2.95

1.053

-5.02, -0.890

0.005

treatment * 3rd

-4.88

1.072

-6.98, -2.78

0.000

Pseudo R square

0.034

shps_arousal

(Intercept)

3.02

0.055

2.91, 3.13

group

control

—

—

—

treatment

0.163

0.078

0.009, 0.316

0.038

time_point

1st

—

—

—

2nd

-0.196

0.059

-0.311, -0.080

0.001

3rd

-0.219

0.060

-0.338, -0.101

0.000

group * time_point

treatment * 2nd

-0.477

0.089

-0.652, -0.302

0.000

treatment * 3rd

-0.565

0.091

-0.743, -0.387

0.000

Pseudo R square

0.112

shps_schedule

(Intercept)

3.53

0.066

3.40, 3.66

group

control

—

—

—

treatment

0.042

0.094

-0.143, 0.226

0.659

time_point

1st

—

—

—

2nd

-0.101

0.060

-0.218, 0.017

0.093

3rd

-0.134

0.061

-0.254, -0.014

0.029

group * time_point

treatment * 2nd

-0.345

0.091

-0.523, -0.167

0.000

treatment * 3rd

-0.423

0.092

-0.604, -0.242

0.000

Pseudo R square

0.045

shps_behavior

(Intercept)

1.99

0.051

1.89, 2.08

group

control

—

—

—

treatment

0.132

0.072

-0.009, 0.273

0.067

time_point

1st

—

—

—

2nd

0.024

0.051

-0.075, 0.124

0.629

3rd

0.009

0.052

-0.092, 0.111

0.857

group * time_point

treatment * 2nd

-0.244

0.077

-0.394, -0.094

0.002

treatment * 3rd

-0.333

0.078

-0.485, -0.180

0.000

Pseudo R square

0.019

shps_environment

(Intercept)

2.33

0.061

2.21, 2.45

group

control

—

—

—

treatment

-0.062

0.086

-0.230, 0.106

0.469

time_point

1st

—

—

—

2nd

-0.058

0.060

-0.175, 0.059

0.331

3rd

-0.057

0.061

-0.177, 0.063

0.352

group * time_point

treatment * 2nd

-0.085

0.091

-0.263, 0.092

0.346

treatment * 3rd

-0.262

0.092

-0.442, -0.081

0.005

Pseudo R square

0.021

dbas_consequence

(Intercept)

6.59

0.140

6.31, 6.86

group

control

—

—

—

treatment

0.054

0.198

-0.335, 0.443

0.787

time_point

1st

—

—

—

2nd

-0.336

0.141

-0.612, -0.061

0.017

3rd

-0.669

0.144

-0.951, -0.388

0.000

group * time_point

treatment * 2nd

-1.11

0.213

-1.53, -0.693

0.000

treatment * 3rd

-1.30

0.217

-1.72, -0.875

0.000

Pseudo R square

0.117

dbas_worry

(Intercept)

14.2

0.283

13.6, 14.8

group

control

—

—

—

treatment

0.341

0.401

-0.445, 1.13

0.395

time_point

1st

—

—

—

2nd

-1.23

0.323

-1.86, -0.599

0.000

3rd

-1.83

0.330

-2.47, -1.18

0.000

group * time_point

treatment * 2nd

-2.71

0.486

-3.66, -1.76

0.000

treatment * 3rd

-2.89

0.494

-3.85, -1.92

0.000

Pseudo R square

0.162

dbas_expectation

(Intercept)

7.17

0.172

6.84, 7.51

group

control

—

—

—

treatment

-0.285

0.244

-0.763, 0.193

0.243

time_point

1st

—

—

—

2nd

-0.343

0.176

-0.687, 0.001

0.051

3rd

-0.768

0.179

-1.12, -0.416

0.000

group * time_point

treatment * 2nd

-1.25

0.266

-1.77, -0.728

0.000

treatment * 3rd

-1.29

0.270

-1.82, -0.758

0.000

Pseudo R square

0.111

dbas_medication

(Intercept)

3.15

0.161

2.83, 3.46

group

control

—

—

—

treatment

0.089

0.228

-0.357, 0.535

0.695

time_point

1st

—

—

—

2nd

0.366

0.164

0.045, 0.688

0.026

3rd

0.309

0.168

-0.020, 0.638

0.066

group * time_point

treatment * 2nd

-0.664

0.248

-1.15, -0.177

0.008

treatment * 3rd

-0.860

0.253

-1.35, -0.365

0.001

Pseudo R square

0.015

psas_somatic

(Intercept)

1.86

0.051

1.76, 1.96

group

control

—

—

—

treatment

0.045

0.072

-0.096, 0.185

0.533

time_point

1st

—

—

—

2nd

0.143

0.047

0.051, 0.236

0.003

3rd

0.011

0.048

-0.084, 0.106

0.815

group * time_point

treatment * 2nd

-0.306

0.072

-0.447, -0.166

0.000

treatment * 3rd

-0.243

0.073

-0.387, -0.100

0.001

Pseudo R square

0.021

psas_cognitive

(Intercept)

2.87

0.063

2.75, 3.00

group

control

—

—

—

treatment

0.099

0.090

-0.077, 0.275

0.269

time_point

1st

—

—

—

2nd

-0.204

0.064

-0.329, -0.079

0.001

3rd

-0.352

0.065

-0.480, -0.224

0.000

group * time_point

treatment * 2nd

-0.434

0.097

-0.623, -0.245

0.000

treatment * 3rd

-0.418

0.098

-0.610, -0.225

0.000

Pseudo R square

0.091

psqi_global

(Intercept)

10.1

0.240

9.67, 10.6

group

control

—

—

—

treatment

0.441

0.339

-0.224, 1.11

0.194

time_point

1st

—

—

—

2nd

-1.22

0.254

-1.72, -0.724

0.000

3rd

-1.27

0.260

-1.78, -0.765

0.000

group * time_point

treatment * 2nd

-1.88

0.384

-2.63, -1.13

0.000

treatment * 3rd

-2.66

0.391

-3.42, -1.89

0.000

Pseudo R square

0.146

mic_attention

(Intercept)

1.30

0.057

1.19, 1.41

group

control

—

—

—

treatment

0.122

0.080

-0.035, 0.278

0.130

time_point

1st

—

—

—

2nd

-0.022

0.055

-0.130, 0.087

0.693

3rd

0.030

0.057

-0.081, 0.141

0.592

group * time_point

treatment * 2nd

-0.248

0.084

-0.412, -0.083

0.003

treatment * 3rd

-0.384

0.085

-0.551, -0.217

0.000

Pseudo R square

0.021

mic_executive

(Intercept)

1.28

0.058

1.17, 1.39

group

control

—

—

—

treatment

0.067

0.082

-0.094, 0.228

0.415

time_point

1st

—

—

—

2nd

-0.034

0.054

-0.140, 0.073

0.537

3rd

-0.046

0.056

-0.155, 0.063

0.410

group * time_point

treatment * 2nd

-0.159

0.082

-0.321, 0.002

0.054

treatment * 3rd

-0.275

0.084

-0.439, -0.110

0.001

Pseudo R square

0.015

mic_memory

(Intercept)

1.33

0.057

1.22, 1.44

group

control

—

—

—

treatment

0.066

0.081

-0.093, 0.224

0.417

time_point

1st

—

—

—

2nd

0.032

0.051

-0.068, 0.131

0.537

3rd

-0.060

0.052

-0.162, 0.042

0.250

group * time_point

treatment * 2nd

-0.276

0.077

-0.428, -0.124

0.000

treatment * 3rd

-0.223

0.079

-0.377, -0.068

0.005

Pseudo R square

0.017

nb_pcs

(Intercept)

46.3

0.659

45.0, 47.6

group

control

—

—

—

treatment

-0.139

0.932

-1.96, 1.69

0.882

time_point

1st

—

—

—

2nd

-0.871

0.590

-2.03, 0.286

0.141

3rd

-0.820

0.604

-2.00, 0.363

0.175

group * time_point

treatment * 2nd

2.76

0.896

1.00, 4.52

0.002

treatment * 3rd

3.24

0.912

1.45, 5.03

0.000

Pseudo R square

0.016

nb_mcs

(Intercept)

39.9

0.770

38.4, 41.4

group

control

—

—

—

treatment

0.085

1.089

-2.05, 2.22

0.938

time_point

1st

—

—

—

2nd

2.00

0.739

0.556, 3.45

0.007

3rd

2.28

0.755

0.801, 3.76

0.003

group * time_point

treatment * 2nd

3.57

1.119

1.38, 5.76

0.002

treatment * 3rd

4.65

1.139

2.42, 6.88

0.000

Pseudo R square

0.056

1SE = Standard Error, CI = Confidence Interval

Text

isi

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict isi with group and time_point (formula: isi ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.26. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.53 (95% CI [12.97, 14.09], t(851) = 47.34, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.92, 0.66], t(851) = -0.32, p = 0.751; Std. beta = -0.03, 95% CI [-0.21, 0.15])
  • The effect of time point [2nd] is statistically significant and negative (beta = -2.46, 95% CI [-3.09, -1.83], t(851) = -7.63, p < .001; Std. beta = -0.55, 95% CI [-0.69, -0.41])
  • The effect of time point [3rd] is statistically significant and negative (beta = -2.87, 95% CI [-3.52, -2.23], t(851) = -8.72, p < .001; Std. beta = -0.64, 95% CI [-0.78, -0.50])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.91, -2.01], t(851) = -6.10, p < .001; Std. beta = -0.66, 95% CI [-0.87, -0.45])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.93, -2.00], t(851) = -6.00, p < .001; Std. beta = -0.66, 95% CI [-0.88, -0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 39.28 (95% CI [36.89, 41.68], t(851) = 32.19, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.65, 95% CI [-2.73, 4.03], t(851) = 0.38, p = 0.707; Std. beta = 0.04, 95% CI [-0.16, 0.24])
  • The effect of time point [2nd] is statistically significant and positive (beta = 2.92, 95% CI [0.58, 5.26], t(851) = 2.44, p = 0.015; Std. beta = 0.17, 95% CI [0.03, 0.31])
  • The effect of time point [3rd] is statistically significant and positive (beta = 3.74, 95% CI [1.35, 6.13], t(851) = 3.06, p = 0.002; Std. beta = 0.22, 95% CI [0.08, 0.36])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 5.61, 95% CI [2.06, 9.15], t(851) = 3.10, p = 0.002; Std. beta = 0.33, 95% CI [0.12, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 6.49, 95% CI [2.88, 10.10], t(851) = 3.53, p < .001; Std. beta = 0.38, 95% CI [0.17, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 8.21 (95% CI [7.47, 8.95], t(851) = 21.71, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.59, 95% CI [-0.46, 1.64], t(851) = 1.11, p = 0.268; Std. beta = 0.11, 95% CI [-0.09, 0.32])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.78, 95% CI [-1.43, -0.13], t(851) = -2.34, p = 0.019; Std. beta = -0.15, 95% CI [-0.28, -0.02])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.66, 95% CI [-1.33, 9.22e-03], t(851) = -1.93, p = 0.053; Std. beta = -0.13, 95% CI [-0.26, 1.78e-03])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.73, 95% CI [-2.73, -0.74], t(851) = -3.43, p < .001; Std. beta = -0.33, 95% CI [-0.53, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.43, 95% CI [-3.43, -1.42], t(851) = -4.71, p < .001; Std. beta = -0.47, 95% CI [-0.66, -0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.54 (95% CI [6.79, 8.28], t(851) = 19.76, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.48, 95% CI [-0.58, 1.54], t(851) = 0.89, p = 0.373; Std. beta = 0.09, 95% CI [-0.11, 0.30])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.44, 95% CI [-1.11, 0.23], t(851) = -1.29, p = 0.197; Std. beta = -0.09, 95% CI [-0.21, 0.04])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.56, 95% CI [-1.24, 0.12], t(851) = -1.61, p = 0.107; Std. beta = -0.11, 95% CI [-0.24, 0.02])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.07, 95% CI [-3.09, -1.06], t(851) = -4.01, p < .001; Std. beta = -0.40, 95% CI [-0.60, -0.20])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.40, 95% CI [-3.43, -1.37], t(851) = -4.56, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

wsas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas with group and time_point (formula: wsas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.65) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 16.77 (95% CI [15.30, 18.24], t(851) = 22.41, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-2.16, 1.99], t(851) = -0.08, p = 0.937; Std. beta = -8.25e-03, 95% CI [-0.21, 0.20])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.18, 0.54], t(851) = -1.18, p = 0.238; Std. beta = -0.08, 95% CI [-0.21, 0.05])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.16, 95% CI [-1.55, 1.24], t(851) = -0.22, p = 0.826; Std. beta = -0.02, 95% CI [-0.15, 0.12])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.95, 95% CI [-5.02, -0.89], t(851) = -2.81, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -4.88, 95% CI [-6.98, -2.78], t(851) = -4.55, p < .001; Std. beta = -0.48, 95% CI [-0.69, -0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_arousal

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_arousal with group and time_point (formula: shps_arousal ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.02 (95% CI [2.91, 3.13], t(851) = 54.51, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 0.16, 95% CI [8.98e-03, 0.32], t(851) = 2.07, p = 0.038; Std. beta = 0.21, 95% CI [0.01, 0.40])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.31, -0.08], t(851) = -3.31, p < .001; Std. beta = -0.25, 95% CI [-0.39, -0.10])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.22, 95% CI [-0.34, -0.10], t(851) = -3.62, p < .001; Std. beta = -0.28, 95% CI [-0.43, -0.13])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.48, 95% CI [-0.65, -0.30], t(851) = -5.34, p < .001; Std. beta = -0.60, 95% CI [-0.83, -0.38])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.56, 95% CI [-0.74, -0.39], t(851) = -6.21, p < .001; Std. beta = -0.72, 95% CI [-0.94, -0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_schedule

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_schedule with group and time_point (formula: shps_schedule ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.53 (95% CI [3.40, 3.66], t(851) = 53.15, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.14, 0.23], t(851) = 0.44, p = 0.659; Std. beta = 0.05, 95% CI [-0.16, 0.25])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.22, 0.02], t(851) = -1.68, p = 0.093; Std. beta = -0.11, 95% CI [-0.24, 0.02])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.13, 95% CI [-0.25, -0.01], t(851) = -2.19, p = 0.029; Std. beta = -0.15, 95% CI [-0.28, -0.02])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.52, -0.17], t(851) = -3.80, p < .001; Std. beta = -0.38, 95% CI [-0.57, -0.18])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.42, 95% CI [-0.60, -0.24], t(851) = -4.57, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_behavior

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_behavior with group and time_point (formula: shps_behavior ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.99 (95% CI [1.89, 2.08], t(851) = 39.03, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.13, 95% CI [-8.77e-03, 0.27], t(851) = 1.84, p = 0.066; Std. beta = 0.19, 95% CI [-0.01, 0.40])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.07, 0.12], t(851) = 0.48, p = 0.629; Std. beta = 0.04, 95% CI [-0.11, 0.18])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 9.30e-03, 95% CI [-0.09, 0.11], t(851) = 0.18, p = 0.857; Std. beta = 0.01, 95% CI [-0.13, 0.16])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.09], t(851) = -3.19, p = 0.001; Std. beta = -0.35, 95% CI [-0.57, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.33, 95% CI [-0.49, -0.18], t(851) = -4.27, p < .001; Std. beta = -0.48, 95% CI [-0.71, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_environment

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_environment with group and time_point (formula: shps_environment ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.33 (95% CI [2.21, 2.45], t(851) = 38.47, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.23, 0.11], t(851) = -0.72, p = 0.469; Std. beta = -0.08, 95% CI [-0.28, 0.13])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(851) = -0.97, p = 0.330; Std. beta = -0.07, 95% CI [-0.22, 0.07])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(851) = -0.93, p = 0.352; Std. beta = -0.07, 95% CI [-0.22, 0.08])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.26, 0.09], t(851) = -0.94, p = 0.346; Std. beta = -0.11, 95% CI [-0.32, 0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.26, 95% CI [-0.44, -0.08], t(851) = -2.84, p = 0.005; Std. beta = -0.32, 95% CI [-0.54, -0.10])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_consequence

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_consequence with group and time_point (formula: dbas_consequence ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.12. The model’s intercept, corresponding to group = control and time_point = 1st, is at 6.59 (95% CI [6.31, 6.86], t(851) = 46.94, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.34, 0.44], t(851) = 0.27, p = 0.787; Std. beta = 0.03, 95% CI [-0.17, 0.22])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.34, 95% CI [-0.61, -0.06], t(851) = -2.39, p = 0.017; Std. beta = -0.17, 95% CI [-0.30, -0.03])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.67, 95% CI [-0.95, -0.39], t(851) = -4.66, p < .001; Std. beta = -0.33, 95% CI [-0.47, -0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.11, 95% CI [-1.53, -0.69], t(851) = -5.22, p < .001; Std. beta = -0.55, 95% CI [-0.76, -0.34])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.30, 95% CI [-1.72, -0.87], t(851) = -6.00, p < .001; Std. beta = -0.65, 95% CI [-0.86, -0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_worry

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_worry with group and time_point (formula: dbas_worry ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.20 (95% CI [13.65, 14.76], t(851) = 50.11, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.44, 1.13], t(851) = 0.85, p = 0.395; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.23, 95% CI [-1.86, -0.60], t(851) = -3.82, p < .001; Std. beta = -0.30, 95% CI [-0.45, -0.14])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.83, 95% CI [-2.47, -1.18], t(851) = -5.54, p < .001; Std. beta = -0.44, 95% CI [-0.60, -0.28])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.71, 95% CI [-3.66, -1.76], t(851) = -5.58, p < .001; Std. beta = -0.65, 95% CI [-0.88, -0.42])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.89, 95% CI [-3.85, -1.92], t(851) = -5.84, p < .001; Std. beta = -0.70, 95% CI [-0.93, -0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_expectation

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_expectation with group and time_point (formula: dbas_expectation ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.17 (95% CI [6.84, 7.51], t(851) = 41.60, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.28, 95% CI [-0.76, 0.19], t(851) = -1.17, p = 0.243; Std. beta = -0.12, 95% CI [-0.31, 0.08])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-0.69, 1.18e-03], t(851) = -1.95, p = 0.051; Std. beta = -0.14, 95% CI [-0.28, 4.82e-04])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.77, 95% CI [-1.12, -0.42], t(851) = -4.28, p < .001; Std. beta = -0.31, 95% CI [-0.46, -0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.25, 95% CI [-1.77, -0.73], t(851) = -4.70, p < .001; Std. beta = -0.51, 95% CI [-0.72, -0.30])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.29, 95% CI [-1.82, -0.76], t(851) = -4.77, p < .001; Std. beta = -0.53, 95% CI [-0.74, -0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_medication

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_medication with group and time_point (formula: dbas_medication ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.15 (95% CI [2.83, 3.46], t(851) = 19.56, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.36, 0.54], t(851) = 0.39, p = 0.694; Std. beta = 0.04, 95% CI [-0.17, 0.25])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.37, 95% CI [0.04, 0.69], t(851) = 2.23, p = 0.026; Std. beta = 0.17, 95% CI [0.02, 0.32])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.31, 95% CI [-0.02, 0.64], t(851) = 1.84, p = 0.065; Std. beta = 0.14, 95% CI [-9.08e-03, 0.30])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.66, 95% CI [-1.15, -0.18], t(851) = -2.67, p = 0.007; Std. beta = -0.31, 95% CI [-0.53, -0.08])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.86, 95% CI [-1.35, -0.36], t(851) = -3.40, p < .001; Std. beta = -0.40, 95% CI [-0.63, -0.17])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_somatic

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_somatic with group and time_point (formula: psas_somatic ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.86 (95% CI [1.76, 1.96], t(851) = 36.76, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.10, 0.19], t(851) = 0.62, p = 0.532; Std. beta = 0.07, 95% CI [-0.14, 0.27])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.14, 95% CI [0.05, 0.24], t(851) = 3.03, p = 0.002; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.01, 95% CI [-0.08, 0.11], t(851) = 0.23, p = 0.815; Std. beta = 0.02, 95% CI [-0.12, 0.16])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.31, 95% CI [-0.45, -0.17], t(851) = -4.27, p < .001; Std. beta = -0.45, 95% CI [-0.65, -0.24])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.10], t(851) = -3.33, p < .001; Std. beta = -0.36, 95% CI [-0.57, -0.15])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_cognitive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_cognitive with group and time_point (formula: psas_cognitive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.87 (95% CI [2.75, 3.00], t(851) = 45.30, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.08, 0.27], t(851) = 1.11, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.31])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.33, -0.08], t(851) = -3.20, p = 0.001; Std. beta = -0.23, 95% CI [-0.37, -0.09])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.35, 95% CI [-0.48, -0.22], t(851) = -5.40, p < .001; Std. beta = -0.40, 95% CI [-0.54, -0.25])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.43, 95% CI [-0.62, -0.24], t(851) = -4.49, p < .001; Std. beta = -0.49, 95% CI [-0.70, -0.28])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.42, 95% CI [-0.61, -0.22], t(851) = -4.25, p < .001; Std. beta = -0.47, 95% CI [-0.69, -0.25])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psqi_global

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psqi_global with group and time_point (formula: psqi_global ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to group = control and time_point = 1st, is at 10.14 (95% CI [9.67, 10.61], t(851) = 42.25, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.44, 95% CI [-0.22, 1.11], t(851) = 1.30, p = 0.194; Std. beta = 0.13, 95% CI [-0.06, 0.32])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.22, 95% CI [-1.72, -0.72], t(851) = -4.81, p < .001; Std. beta = -0.35, 95% CI [-0.49, -0.21])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.27, 95% CI [-1.78, -0.76], t(851) = -4.90, p < .001; Std. beta = -0.37, 95% CI [-0.51, -0.22])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.88, 95% CI [-2.63, -1.13], t(851) = -4.90, p < .001; Std. beta = -0.54, 95% CI [-0.76, -0.32])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.66, 95% CI [-3.42, -1.89], t(851) = -6.79, p < .001; Std. beta = -0.76, 95% CI [-0.98, -0.54])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_attention

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_attention with group and time_point (formula: mic_attention ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.30 (95% CI [1.19, 1.41], t(851) = 22.92, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.04, 0.28], t(851) = 1.52, p = 0.129; Std. beta = 0.16, 95% CI [-0.05, 0.36])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.13, 0.09], t(851) = -0.40, p = 0.693; Std. beta = -0.03, 95% CI [-0.17, 0.11])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.08, 0.14], t(851) = 0.54, p = 0.592; Std. beta = 0.04, 95% CI [-0.11, 0.18])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.25, 95% CI [-0.41, -0.08], t(851) = -2.95, p = 0.003; Std. beta = -0.32, 95% CI [-0.54, -0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.38, 95% CI [-0.55, -0.22], t(851) = -4.50, p < .001; Std. beta = -0.50, 95% CI [-0.72, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_executive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_executive with group and time_point (formula: mic_executive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.28 (95% CI [1.17, 1.39], t(851) = 22.01, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.23], t(851) = 0.82, p = 0.415; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.14, 0.07], t(851) = -0.62, p = 0.536; Std. beta = -0.04, 95% CI [-0.18, 0.09])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.15, 0.06], t(851) = -0.82, p = 0.409; Std. beta = -0.06, 95% CI [-0.20, 0.08])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.32, 2.22e-03], t(851) = -1.93, p = 0.053; Std. beta = -0.20, 95% CI [-0.41, 2.81e-03])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.27, 95% CI [-0.44, -0.11], t(851) = -3.27, p = 0.001; Std. beta = -0.35, 95% CI [-0.56, -0.14])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_memory

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_memory with group and time_point (formula: mic_memory ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.33 (95% CI [1.22, 1.44], t(851) = 23.31, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.22], t(851) = 0.81, p = 0.417; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.07, 0.13], t(851) = 0.62, p = 0.537; Std. beta = 0.04, 95% CI [-0.09, 0.17])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.16, 0.04], t(851) = -1.15, p = 0.249; Std. beta = -0.08, 95% CI [-0.21, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.28, 95% CI [-0.43, -0.12], t(851) = -3.56, p < .001; Std. beta = -0.36, 95% CI [-0.55, -0.16])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.22, 95% CI [-0.38, -0.07], t(851) = -2.83, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 46.33 (95% CI [45.04, 47.63], t(851) = 70.33, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.96, 1.69], t(851) = -0.15, p = 0.882; Std. beta = -0.02, 95% CI [-0.22, 0.19])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.87, 95% CI [-2.03, 0.29], t(851) = -1.48, p = 0.140; Std. beta = -0.10, 95% CI [-0.23, 0.03])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.00, 0.36], t(851) = -1.36, p = 0.174; Std. beta = -0.09, 95% CI [-0.22, 0.04])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 2.76, 95% CI [1.00, 4.52], t(851) = 3.08, p = 0.002; Std. beta = 0.31, 95% CI [0.11, 0.51])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 3.24, 95% CI [1.45, 5.03], t(851) = 3.55, p < .001; Std. beta = 0.36, 95% CI [0.16, 0.56])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 39.90 (95% CI [38.39, 41.41], t(851) = 51.80, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-2.05, 2.22], t(851) = 0.08, p = 0.938; Std. beta = 7.99e-03, 95% CI [-0.19, 0.21])
  • The effect of time point [2nd] is statistically significant and positive (beta = 2.00, 95% CI [0.56, 3.45], t(851) = 2.71, p = 0.007; Std. beta = 0.19, 95% CI [0.05, 0.32])
  • The effect of time point [3rd] is statistically significant and positive (beta = 2.28, 95% CI [0.80, 3.76], t(851) = 3.02, p = 0.003; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 3.57, 95% CI [1.38, 5.76], t(851) = 3.19, p = 0.001; Std. beta = 0.33, 95% CI [0.13, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 4.65, 95% CI [2.42, 6.88], t(851) = 4.08, p < .001; Std. beta = 0.44, 95% CI [0.23, 0.64])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

isi

null

3

4,948.816

4,963.083

-2,471.408

4,942.816

isi

random

8

4,615.080

4,653.126

-2,299.540

4,599.080

343.736

5

0.000

who

null

3

7,061.290

7,075.557

-3,527.645

7,055.290

who

random

8

6,995.494

7,033.540

-3,489.747

6,979.494

75.796

5

0.000

phq

null

3

4,958.134

4,972.401

-2,476.067

4,952.134

phq

random

8

4,891.540

4,929.586

-2,437.770

4,875.540

76.594

5

0.000

gad

null

3

4,976.571

4,990.838

-2,485.286

4,970.571

gad

random

8

4,918.777

4,956.823

-2,451.389

4,902.777

67.794

5

0.000

wsas

null

3

6,146.880

6,161.148

-3,070.440

6,140.880

wsas

random

8

6,109.472

6,147.518

-3,046.736

6,093.472

47.408

5

0.000

shps_arousal

null

3

1,906.464

1,920.731

-950.232

1,900.464

shps_arousal

random

8

1,754.921

1,792.967

-869.461

1,738.921

161.543

5

0.000

shps_schedule

null

3

1,992.302

2,006.569

-993.151

1,986.302

shps_schedule

random

8

1,924.626

1,962.673

-954.313

1,908.626

77.675

5

0.000

shps_behavior

null

3

1,572.223

1,586.490

-783.111

1,566.223

shps_behavior

random

8

1,549.508

1,587.554

-766.754

1,533.508

32.715

5

0.000

shps_environment

null

3

1,860.080

1,874.347

-927.040

1,854.080

shps_environment

random

8

1,844.650

1,882.696

-914.325

1,828.650

25.430

5

0.000

dbas_consequence

null

3

3,459.555

3,473.822

-1,726.777

3,453.555

dbas_consequence

random

8

3,299.461

3,337.508

-1,641.731

3,283.461

170.093

5

0.000

dbas_worry

null

3

4,801.989

4,816.256

-2,397.995

4,795.989

dbas_worry

random

8

4,607.566

4,645.613

-2,295.783

4,591.566

204.423

5

0.000

dbas_expectation

null

3

3,794.146

3,808.413

-1,894.073

3,788.146

dbas_expectation

random

8

3,666.551

3,704.597

-1,825.276

3,650.551

137.594

5

0.000

dbas_medication

null

3

3,555.458

3,569.725

-1,774.729

3,549.458

dbas_medication

random

8

3,548.887

3,586.934

-1,766.444

3,532.887

16.571

5

0.005

psas_somatic

null

3

1,511.020

1,525.288

-752.510

1,505.020

psas_somatic

random

8

1,489.176

1,527.222

-736.588

1,473.176

31.844

5

0.000

psas_cognitive

null

3

2,071.703

2,085.970

-1,032.852

2,065.703

psas_cognitive

random

8

1,938.230

1,976.276

-961.115

1,922.230

143.473

5

0.000

psqi_global

null

3

4,467.524

4,481.792

-2,230.762

4,461.524

psqi_global

random

8

4,266.778

4,304.824

-2,125.389

4,250.778

210.747

5

0.000

mic_attention

null

3

1,744.527

1,758.794

-869.263

1,738.527

mic_attention

random

8

1,719.760

1,757.806

-851.880

1,703.760

34.767

5

0.000

mic_executive

null

3

1,743.908

1,758.175

-868.954

1,737.908

mic_executive

random

8

1,726.611

1,764.657

-855.305

1,710.611

27.297

5

0.000

mic_memory

null

3

1,677.918

1,692.186

-835.959

1,671.918

mic_memory

random

8

1,657.068

1,695.114

-820.534

1,641.068

30.850

5

0.000

nb_pcs

null

3

5,869.922

5,884.189

-2,931.961

5,863.922

nb_pcs

random

8

5,860.727

5,898.773

-2,922.363

5,844.727

19.195

5

0.002

nb_mcs

null

3

6,263.319

6,277.587

-3,128.660

6,257.319

nb_mcs

random

8

6,187.789

6,225.835

-3,085.895

6,171.789

85.530

5

0.000

Post hoc analysis

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

isi

1st

179

13.53 ± 3.82

179

13.40 ± 3.82

0.751

0.045

isi

2nd

148

11.07 ± 3.76

0.865

109

7.98 ± 3.67

1.908

0.000

1.088

isi

3rd

139

10.66 ± 3.73

1.011

105

7.57 ± 3.66

2.054

0.000

1.089

who

1st

179

39.28 ± 16.33

179

39.93 ± 16.33

0.707

-0.062

who

2nd

148

42.20 ± 15.83

-0.279

109

48.46 ± 15.18

-0.815

0.001

-0.598

who

3rd

139

43.03 ± 15.64

-0.358

105

50.17 ± 15.09

-0.979

0.000

-0.683

phq

1st

179

8.21 ± 5.06

179

8.80 ± 5.06

0.269

-0.204

phq

2nd

148

7.43 ± 4.86

0.268

109

6.29 ± 4.60

0.865

0.055

0.393

phq

3rd

139

7.55 ± 4.79

0.227

105

5.72 ± 4.56

1.062

0.002

0.631

gad

1st

179

7.54 ± 5.10

179

8.02 ± 5.10

0.373

-0.162

gad

2nd

148

7.10 ± 4.91

0.148

109

5.50 ± 4.65

0.844

0.008

0.535

gad

3rd

139

6.98 ± 4.83

0.189

105

5.05 ± 4.61

0.996

0.002

0.646

wsas

1st

179

16.77 ± 10.01

179

16.69 ± 10.01

0.937

0.014

wsas

2nd

148

15.95 ± 9.66

0.135

109

12.92 ± 9.20

0.622

0.011

0.501

wsas

3rd

139

16.62 ± 9.53

0.026

105

11.65 ± 9.13

0.830

0.000

0.818

shps_arousal

1st

179

3.02 ± 0.74

179

3.18 ± 0.74

0.038

-0.313

shps_arousal

2nd

148

2.83 ± 0.73

0.376

109

2.51 ± 0.70

1.294

0.000

0.605

shps_arousal

3rd

139

2.80 ± 0.72

0.421

105

2.40 ± 0.70

1.508

0.000

0.773

shps_schedule

1st

179

3.53 ± 0.89

179

3.58 ± 0.89

0.659

-0.079

shps_schedule

2nd

148

3.43 ± 0.86

0.193

109

3.13 ± 0.81

0.852

0.004

0.580

shps_schedule

3rd

139

3.40 ± 0.84

0.256

105

3.02 ± 0.81

1.066

0.000

0.730

shps_behavior

1st

179

1.99 ± 0.68

179

2.12 ± 0.68

0.067

-0.298

shps_behavior

2nd

148

2.01 ± 0.66

-0.055

109

1.90 ± 0.64

0.495

0.172

0.252

shps_behavior

3rd

139

1.99 ± 0.65

-0.021

105

1.79 ± 0.63

0.730

0.016

0.453

shps_environment

1st

179

2.33 ± 0.81

179

2.27 ± 0.81

0.469

0.119

shps_environment

2nd

148

2.27 ± 0.79

0.111

109

2.13 ± 0.76

0.274

0.129

0.282

shps_environment

3rd

139

2.28 ± 0.78

0.109

105

1.95 ± 0.75

0.608

0.001

0.618

dbas_consequence

1st

179

6.59 ± 1.88

179

6.64 ± 1.88

0.787

-0.044

dbas_consequence

2nd

148

6.25 ± 1.82

0.273

109

5.19 ± 1.76

1.174

0.000

0.858

dbas_consequence

3rd

139

5.92 ± 1.80

0.543

105

4.67 ± 1.74

1.597

0.000

1.011

dbas_worry

1st

179

14.20 ± 3.79

179

14.54 ± 3.79

0.395

-0.120

dbas_worry

2nd

148

12.97 ± 3.73

0.433

109

10.60 ± 3.65

1.386

0.000

0.834

dbas_worry

3rd

139

12.37 ± 3.70

0.642

105

9.83 ± 3.64

1.657

0.000

0.894

dbas_expectation

1st

179

7.17 ± 2.31

179

6.89 ± 2.31

0.243

0.185

dbas_expectation

2nd

148

6.83 ± 2.25

0.223

109

5.30 ± 2.16

1.033

0.000

0.996

dbas_expectation

3rd

139

6.41 ± 2.22

0.499

105

4.83 ± 2.15

1.335

0.000

1.022

dbas_medication

1st

179

3.15 ± 2.15

179

3.24 ± 2.15

0.695

-0.062

dbas_medication

2nd

148

3.51 ± 2.10

-0.255

109

2.94 ± 2.02

0.207

0.027

0.399

dbas_medication

3rd

139

3.46 ± 2.07

-0.215

105

2.69 ± 2.01

0.383

0.004

0.535

psas_somatic

1st

179

1.86 ± 0.68

179

1.91 ± 0.68

0.533

-0.108

psas_somatic

2nd

148

2.00 ± 0.65

-0.347

109

1.74 ± 0.62

0.393

0.001

0.632

psas_somatic

3rd

139

1.87 ± 0.65

-0.027

105

1.67 ± 0.62

0.561

0.015

0.480

psas_cognitive

1st

179

2.87 ± 0.85

179

2.97 ± 0.85

0.269

-0.177

psas_cognitive

2nd

148

2.67 ± 0.82

0.365

109

2.33 ± 0.79

1.141

0.001

0.599

psas_cognitive

3rd

139

2.52 ± 0.82

0.630

105

2.20 ± 0.79

1.376

0.002

0.569

psqi_global

1st

179

10.14 ± 3.21

179

10.58 ± 3.21

0.194

-0.198

psqi_global

2nd

148

8.92 ± 3.14

0.547

109

7.48 ± 3.04

1.389

0.000

0.644

psqi_global

3rd

139

8.87 ± 3.11

0.570

105

6.65 ± 3.03

1.759

0.000

0.991

mic_attention

1st

179

1.30 ± 0.76

179

1.42 ± 0.76

0.130

-0.251

mic_attention

2nd

148

1.28 ± 0.73

0.045

109

1.15 ± 0.70

0.556

0.164

0.260

mic_attention

3rd

139

1.33 ± 0.73

-0.063

105

1.07 ± 0.70

0.729

0.004

0.541

mic_executive

1st

179

1.28 ± 0.78

179

1.35 ± 0.78

0.415

-0.141

mic_executive

2nd

148

1.25 ± 0.75

0.071

109

1.15 ± 0.72

0.406

0.318

0.194

mic_executive

3rd

139

1.23 ± 0.74

0.096

105

1.03 ± 0.71

0.674

0.027

0.437

mic_memory

1st

179

1.33 ± 0.76

179

1.40 ± 0.76

0.417

-0.147

mic_memory

2nd

148

1.36 ± 0.74

-0.071

109

1.15 ± 0.70

0.549

0.020

0.472

mic_memory

3rd

139

1.27 ± 0.72

0.135

105

1.12 ± 0.69

0.636

0.085

0.353

nb_pcs

1st

179

46.33 ± 8.81

179

46.20 ± 8.81

0.882

0.027

nb_pcs

2nd

148

45.46 ± 8.48

0.169

109

48.08 ± 8.04

-0.366

0.012

-0.508

nb_pcs

3rd

139

45.51 ± 8.35

0.159

105

48.61 ± 7.97

-0.469

0.003

-0.601

nb_mcs

1st

179

39.90 ± 10.30

179

39.98 ± 10.30

0.938

-0.013

nb_mcs

2nd

148

41.90 ± 9.97

-0.310

109

45.56 ± 9.54

-0.862

0.003

-0.565

nb_mcs

3rd

139

42.18 ± 9.84

-0.353

105

46.91 ± 9.47

-1.072

0.000

-0.733

Between group

isi

1st

t(640.51) = -0.32, p = 0.751, Cohen d = 0.05, 95% CI (-0.92 to 0.67)

2st

t(756.98) = -6.60, p = 0.000, Cohen d = 1.09, 95% CI (-4.01 to -2.17)

3rd

t(772.27) = -6.48, p = 0.000, Cohen d = 1.09, 95% CI (-4.03 to -2.16)

who

1st

t(548.38) = 0.38, p = 0.707, Cohen d = -0.06, 95% CI (-2.74 to 4.04)

2st

t(687.48) = 3.20, p = 0.001, Cohen d = -0.60, 95% CI (2.42 to 10.09)

3rd

t(705.91) = 3.60, p = 0.000, Cohen d = -0.68, 95% CI (3.25 to 11.03)

phq

1st

t(501.28) = 1.11, p = 0.269, Cohen d = -0.20, 95% CI (-0.46 to 1.64)

2st

t(637.10) = -1.92, p = 0.055, Cohen d = 0.39, 95% CI (-2.31 to 0.03)

3rd

t(655.28) = -3.04, p = 0.002, Cohen d = 0.63, 95% CI (-3.02 to -0.65)

gad

1st

t(507.04) = 0.89, p = 0.373, Cohen d = -0.16, 95% CI (-0.58 to 1.54)

2st

t(644.00) = -2.65, p = 0.008, Cohen d = 0.54, 95% CI (-2.77 to -0.41)

3rd

t(662.32) = -3.16, p = 0.002, Cohen d = 0.65, 95% CI (-3.12 to -0.73)

wsas

1st

t(522.74) = -0.08, p = 0.937, Cohen d = 0.01, 95% CI (-2.16 to 2.00)

2st

t(661.70) = -2.56, p = 0.011, Cohen d = 0.50, 95% CI (-5.37 to -0.71)

3rd

t(680.22) = -4.13, p = 0.000, Cohen d = 0.82, 95% CI (-7.32 to -2.60)

shps_arousal

1st

t(600.25) = 2.07, p = 0.038, Cohen d = -0.31, 95% CI (0.01 to 0.32)

2st

t(730.24) = -3.50, p = 0.000, Cohen d = 0.61, 95% CI (-0.49 to -0.14)

3rd

t(747.32) = -4.39, p = 0.000, Cohen d = 0.77, 95% CI (-0.58 to -0.22)

shps_schedule

1st

t(510.10) = 0.44, p = 0.659, Cohen d = -0.08, 95% CI (-0.14 to 0.23)

2st

t(647.57) = -2.89, p = 0.004, Cohen d = 0.58, 95% CI (-0.51 to -0.10)

3rd

t(665.94) = -3.59, p = 0.000, Cohen d = 0.73, 95% CI (-0.59 to -0.17)

shps_behavior

1st

t(556.98) = 1.84, p = 0.067, Cohen d = -0.30, 95% CI (-0.01 to 0.27)

2st

t(695.37) = -1.37, p = 0.172, Cohen d = 0.25, 95% CI (-0.27 to 0.05)

3rd

t(713.67) = -2.42, p = 0.016, Cohen d = 0.45, 95% CI (-0.36 to -0.04)

shps_environment

1st

t(552.78) = -0.72, p = 0.469, Cohen d = 0.12, 95% CI (-0.23 to 0.11)

2st

t(691.56) = -1.52, p = 0.129, Cohen d = 0.28, 95% CI (-0.34 to 0.04)

3rd

t(709.92) = -3.29, p = 0.001, Cohen d = 0.62, 95% CI (-0.52 to -0.13)

dbas_consequence

1st

t(561.19) = 0.27, p = 0.787, Cohen d = -0.04, 95% CI (-0.34 to 0.44)

2st

t(699.10) = -4.69, p = 0.000, Cohen d = 0.86, 95% CI (-1.50 to -0.61)

3rd

t(717.32) = -5.44, p = 0.000, Cohen d = 1.01, 95% CI (-1.69 to -0.80)

dbas_worry

1st

t(648.14) = 0.85, p = 0.395, Cohen d = -0.12, 95% CI (-0.45 to 1.13)

2st

t(761.55) = -5.10, p = 0.000, Cohen d = 0.83, 95% CI (-3.28 to -1.46)

3rd

t(776.46) = -5.37, p = 0.000, Cohen d = 0.89, 95% CI (-3.47 to -1.61)

dbas_expectation

1st

t(570.48) = -1.17, p = 0.243, Cohen d = 0.19, 95% CI (-0.76 to 0.19)

2st

t(707.06) = -5.52, p = 0.000, Cohen d = 1.00, 95% CI (-2.08 to -0.99)

3rd

t(725.07) = -5.58, p = 0.000, Cohen d = 1.02, 95% CI (-2.13 to -1.02)

dbas_medication

1st

t(571.42) = 0.39, p = 0.695, Cohen d = -0.06, 95% CI (-0.36 to 0.54)

2st

t(707.85) = -2.22, p = 0.027, Cohen d = 0.40, 95% CI (-1.08 to -0.07)

3rd

t(725.84) = -2.93, p = 0.004, Cohen d = 0.54, 95% CI (-1.29 to -0.25)

psas_somatic

1st

t(526.35) = 0.62, p = 0.533, Cohen d = -0.11, 95% CI (-0.10 to 0.19)

2st

t(665.55) = -3.26, p = 0.001, Cohen d = 0.63, 95% CI (-0.42 to -0.10)

3rd

t(684.09) = -2.44, p = 0.015, Cohen d = 0.48, 95% CI (-0.36 to -0.04)

psas_cognitive

1st

t(563.72) = 1.11, p = 0.269, Cohen d = -0.18, 95% CI (-0.08 to 0.28)

2st

t(701.31) = -3.29, p = 0.001, Cohen d = 0.60, 95% CI (-0.53 to -0.13)

3rd

t(719.48) = -3.08, p = 0.002, Cohen d = 0.57, 95% CI (-0.52 to -0.12)

psqi_global

1st

t(595.32) = 1.30, p = 0.194, Cohen d = -0.20, 95% CI (-0.23 to 1.11)

2st

t(726.63) = -3.70, p = 0.000, Cohen d = 0.64, 95% CI (-2.20 to -0.68)

3rd

t(743.89) = -5.60, p = 0.000, Cohen d = 0.99, 95% CI (-2.99 to -1.44)

mic_attention

1st

t(548.17) = 1.52, p = 0.130, Cohen d = -0.25, 95% CI (-0.04 to 0.28)

2st

t(687.28) = -1.39, p = 0.164, Cohen d = 0.26, 95% CI (-0.30 to 0.05)

3rd

t(705.72) = -2.85, p = 0.004, Cohen d = 0.54, 95% CI (-0.44 to -0.08)

mic_executive

1st

t(526.24) = 0.82, p = 0.415, Cohen d = -0.14, 95% CI (-0.09 to 0.23)

2st

t(665.43) = -1.00, p = 0.318, Cohen d = 0.19, 95% CI (-0.27 to 0.09)

3rd

t(683.97) = -2.22, p = 0.027, Cohen d = 0.44, 95% CI (-0.39 to -0.02)

mic_memory

1st

t(506.57) = 0.81, p = 0.417, Cohen d = -0.15, 95% CI (-0.09 to 0.22)

2st

t(643.44) = -2.33, p = 0.020, Cohen d = 0.47, 95% CI (-0.39 to -0.03)

3rd

t(661.75) = -1.72, p = 0.085, Cohen d = 0.35, 95% CI (-0.34 to 0.02)

nb_pcs

1st

t(508.29) = -0.15, p = 0.882, Cohen d = 0.03, 95% CI (-1.97 to 1.69)

2st

t(645.46) = 2.52, p = 0.012, Cohen d = -0.51, 95% CI (0.58 to 4.66)

3rd

t(663.80) = 2.94, p = 0.003, Cohen d = -0.60, 95% CI (1.03 to 5.16)

nb_mcs

1st

t(538.35) = 0.08, p = 0.938, Cohen d = -0.01, 95% CI (-2.05 to 2.22)

2st

t(677.82) = 2.98, p = 0.003, Cohen d = -0.57, 95% CI (1.24 to 6.07)

3rd

t(696.34) = 3.80, p = 0.000, Cohen d = -0.73, 95% CI (2.29 to 7.18)

Within treatment group

isi

1st vs 2st

t(597.72) = -14.91, p = 0.000, Cohen d = 1.91, 95% CI (-6.13 to -4.71)

1st vs 3rd

t(599.39) = -15.83, p = 0.000, Cohen d = 2.05, 95% CI (-6.56 to -5.11)

who

1st vs 2st

t(574.56) = 6.27, p = 0.000, Cohen d = -0.82, 95% CI (5.85 to 11.19)

1st vs 3rd

t(575.30) = 7.42, p = 0.000, Cohen d = -0.98, 95% CI (7.53 to 12.94)

phq

1st vs 2st

t(560.18) = -6.60, p = 0.000, Cohen d = 0.86, 95% CI (-3.26 to -1.76)

1st vs 3rd

t(560.56) = -7.99, p = 0.000, Cohen d = 1.06, 95% CI (-3.84 to -2.33)

gad

1st vs 2st

t(562.06) = -6.45, p = 0.000, Cohen d = 0.84, 95% CI (-3.28 to -1.75)

1st vs 3rd

t(562.48) = -7.50, p = 0.000, Cohen d = 1.00, 95% CI (-3.74 to -2.19)

wsas

1st vs 2st

t(567.00) = -4.76, p = 0.000, Cohen d = 0.62, 95% CI (-5.33 to -2.22)

1st vs 3rd

t(567.53) = -6.27, p = 0.000, Cohen d = 0.83, 95% CI (-6.61 to -3.46)

shps_arousal

1st vs 2st

t(588.28) = -10.04, p = 0.000, Cohen d = 1.29, 95% CI (-0.80 to -0.54)

1st vs 3rd

t(589.49) = -11.54, p = 0.000, Cohen d = 1.51, 95% CI (-0.92 to -0.65)

shps_schedule

1st vs 2st

t(563.04) = -6.51, p = 0.000, Cohen d = 0.85, 95% CI (-0.58 to -0.31)

1st vs 3rd

t(563.48) = -8.03, p = 0.000, Cohen d = 1.07, 95% CI (-0.69 to -0.42)

shps_behavior

1st vs 2st

t(576.98) = -3.81, p = 0.000, Cohen d = 0.50, 95% CI (-0.33 to -0.11)

1st vs 3rd

t(577.78) = -5.55, p = 0.000, Cohen d = 0.73, 95% CI (-0.44 to -0.21)

shps_environment

1st vs 2st

t(575.81) = -2.11, p = 0.071, Cohen d = 0.27, 95% CI (-0.28 to -0.01)

1st vs 3rd

t(576.57) = -4.62, p = 0.000, Cohen d = 0.61, 95% CI (-0.45 to -0.18)

dbas_consequence

1st vs 2st

t(578.14) = -9.05, p = 0.000, Cohen d = 1.17, 95% CI (-1.76 to -1.13)

1st vs 3rd

t(578.98) = -12.14, p = 0.000, Cohen d = 1.60, 95% CI (-2.29 to -1.65)

dbas_worry

1st vs 2st

t(599.41) = -10.84, p = 0.000, Cohen d = 1.39, 95% CI (-4.66 to -3.23)

1st vs 3rd

t(601.17) = -12.78, p = 0.000, Cohen d = 1.66, 95% CI (-5.44 to -3.99)

dbas_expectation

1st vs 2st

t(580.65) = -7.98, p = 0.000, Cohen d = 1.03, 95% CI (-1.98 to -1.20)

1st vs 3rd

t(581.57) = -10.17, p = 0.000, Cohen d = 1.34, 95% CI (-2.45 to -1.66)

dbas_medication

1st vs 2st

t(580.90) = -1.60, p = 0.222, Cohen d = 0.21, 95% CI (-0.66 to 0.07)

1st vs 3rd

t(581.83) = -2.92, p = 0.007, Cohen d = 0.38, 95% CI (-0.92 to -0.18)

psas_somatic

1st vs 2st

t(568.10) = -3.01, p = 0.005, Cohen d = 0.39, 95% CI (-0.27 to -0.06)

1st vs 3rd

t(568.65) = -4.24, p = 0.000, Cohen d = 0.56, 95% CI (-0.34 to -0.12)

psas_cognitive

1st vs 2st

t(578.83) = -8.80, p = 0.000, Cohen d = 1.14, 95% CI (-0.78 to -0.50)

1st vs 3rd

t(579.69) = -10.47, p = 0.000, Cohen d = 1.38, 95% CI (-0.91 to -0.63)

psqi_global

1st vs 2st

t(587.05) = -10.77, p = 0.000, Cohen d = 1.39, 95% CI (-3.67 to -2.54)

1st vs 3rd

t(588.22) = -13.45, p = 0.000, Cohen d = 1.76, 95% CI (-4.50 to -3.36)

mic_attention

1st vs 2st

t(574.50) = -4.27, p = 0.000, Cohen d = 0.56, 95% CI (-0.39 to -0.15)

1st vs 3rd

t(575.23) = -5.53, p = 0.000, Cohen d = 0.73, 95% CI (-0.48 to -0.23)

mic_executive

1st vs 2st

t(568.06) = -3.11, p = 0.004, Cohen d = 0.41, 95% CI (-0.31 to -0.07)

1st vs 3rd

t(568.62) = -5.10, p = 0.000, Cohen d = 0.67, 95% CI (-0.44 to -0.20)

mic_memory

1st vs 2st

t(561.90) = -4.19, p = 0.000, Cohen d = 0.55, 95% CI (-0.36 to -0.13)

1st vs 3rd

t(562.32) = -4.79, p = 0.000, Cohen d = 0.64, 95% CI (-0.40 to -0.17)

nb_pcs

1st vs 2st

t(562.46) = 2.80, p = 0.011, Cohen d = -0.37, 95% CI (0.56 to 3.21)

1st vs 3rd

t(562.89) = 3.53, p = 0.001, Cohen d = -0.47, 95% CI (1.07 to 3.76)

nb_mcs

1st vs 2st

t(571.67) = 6.62, p = 0.000, Cohen d = -0.86, 95% CI (3.92 to 7.23)

1st vs 3rd

t(572.32) = 8.12, p = 0.000, Cohen d = -1.07, 95% CI (5.25 to 8.61)

Within control group

isi

1st vs 2st

t(544.64) = -7.63, p = 0.000, Cohen d = 0.87, 95% CI (-3.09 to -1.82)

1st vs 3rd

t(549.35) = -8.72, p = 0.000, Cohen d = 1.01, 95% CI (-3.52 to -2.22)

who

1st vs 2st

t(532.59) = 2.44, p = 0.030, Cohen d = -0.28, 95% CI (0.57 to 5.26)

1st vs 3rd

t(535.51) = 3.06, p = 0.005, Cohen d = -0.36, 95% CI (1.34 to 6.14)

phq

1st vs 2st

t(525.62) = -2.34, p = 0.039, Cohen d = 0.27, 95% CI (-1.43 to -0.12)

1st vs 3rd

t(527.72) = -1.93, p = 0.108, Cohen d = 0.23, 95% CI (-1.33 to 0.01)

gad

1st vs 2st

t(526.51) = -1.29, p = 0.395, Cohen d = 0.15, 95% CI (-1.11 to 0.23)

1st vs 3rd

t(528.71) = -1.61, p = 0.216, Cohen d = 0.19, 95% CI (-1.25 to 0.12)

wsas

1st vs 2st

t(528.89) = -1.18, p = 0.477, Cohen d = 0.14, 95% CI (-2.18 to 0.54)

1st vs 3rd

t(531.36) = -0.22, p = 1.000, Cohen d = 0.03, 95% CI (-1.55 to 1.24)

shps_arousal

1st vs 2st

t(539.56) = -3.31, p = 0.002, Cohen d = 0.38, 95% CI (-0.31 to -0.08)

1st vs 3rd

t(543.46) = -3.62, p = 0.001, Cohen d = 0.42, 95% CI (-0.34 to -0.10)

shps_schedule

1st vs 2st

t(526.98) = -1.68, p = 0.187, Cohen d = 0.19, 95% CI (-0.22 to 0.02)

1st vs 3rd

t(529.24) = -2.19, p = 0.058, Cohen d = 0.26, 95% CI (-0.25 to -0.01)

shps_behavior

1st vs 2st

t(533.78) = 0.48, p = 1.000, Cohen d = -0.06, 95% CI (-0.07 to 0.12)

1st vs 3rd

t(536.87) = 0.18, p = 1.000, Cohen d = -0.02, 95% CI (-0.09 to 0.11)

shps_environment

1st vs 2st

t(533.20) = -0.97, p = 0.661, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

1st vs 3rd

t(536.21) = -0.93, p = 0.705, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

dbas_consequence

1st vs 2st

t(534.36) = -2.39, p = 0.034, Cohen d = 0.27, 95% CI (-0.61 to -0.06)

1st vs 3rd

t(537.52) = -4.65, p = 0.000, Cohen d = 0.54, 95% CI (-0.95 to -0.39)

dbas_worry

1st vs 2st

t(545.58) = -3.82, p = 0.000, Cohen d = 0.43, 95% CI (-1.87 to -0.60)

1st vs 3rd

t(550.45) = -5.54, p = 0.000, Cohen d = 0.64, 95% CI (-2.47 to -1.18)

dbas_expectation

1st vs 2st

t(535.63) = -1.95, p = 0.103, Cohen d = 0.22, 95% CI (-0.69 to 0.00)

1st vs 3rd

t(538.96) = -4.28, p = 0.000, Cohen d = 0.50, 95% CI (-1.12 to -0.41)

dbas_medication

1st vs 2st

t(535.76) = 2.23, p = 0.052, Cohen d = -0.25, 95% CI (0.04 to 0.69)

1st vs 3rd

t(539.11) = 1.84, p = 0.132, Cohen d = -0.21, 95% CI (-0.02 to 0.64)

psas_somatic

1st vs 2st

t(529.42) = 3.03, p = 0.005, Cohen d = -0.35, 95% CI (0.05 to 0.24)

1st vs 3rd

t(531.95) = 0.23, p = 1.000, Cohen d = -0.03, 95% CI (-0.08 to 0.11)

psas_cognitive

1st vs 2st

t(534.71) = -3.20, p = 0.003, Cohen d = 0.37, 95% CI (-0.33 to -0.08)

1st vs 3rd

t(537.92) = -5.40, p = 0.000, Cohen d = 0.63, 95% CI (-0.48 to -0.22)

psqi_global

1st vs 2st

t(538.92) = -4.81, p = 0.000, Cohen d = 0.55, 95% CI (-1.72 to -0.72)

1st vs 3rd

t(542.72) = -4.90, p = 0.000, Cohen d = 0.57, 95% CI (-1.78 to -0.76)

mic_attention

1st vs 2st

t(532.56) = -0.40, p = 1.000, Cohen d = 0.05, 95% CI (-0.13 to 0.09)

1st vs 3rd

t(535.48) = 0.54, p = 1.000, Cohen d = -0.06, 95% CI (-0.08 to 0.14)

mic_executive

1st vs 2st

t(529.40) = -0.62, p = 1.000, Cohen d = 0.07, 95% CI (-0.14 to 0.07)

1st vs 3rd

t(531.93) = -0.82, p = 0.820, Cohen d = 0.10, 95% CI (-0.16 to 0.06)

mic_memory

1st vs 2st

t(526.44) = 0.62, p = 1.000, Cohen d = -0.07, 95% CI (-0.07 to 0.13)

1st vs 3rd

t(528.63) = -1.15, p = 0.500, Cohen d = 0.13, 95% CI (-0.16 to 0.04)

nb_pcs

1st vs 2st

t(526.71) = -1.48, p = 0.281, Cohen d = 0.17, 95% CI (-2.03 to 0.29)

1st vs 3rd

t(528.93) = -1.36, p = 0.350, Cohen d = 0.16, 95% CI (-2.01 to 0.37)

nb_mcs

1st vs 2st

t(531.16) = 2.71, p = 0.014, Cohen d = -0.31, 95% CI (0.55 to 3.45)

1st vs 3rd

t(533.91) = 3.02, p = 0.005, Cohen d = -0.35, 95% CI (0.80 to 3.77)

Plot

Clinical significance

T1

T2

T3

outcome

control1

treatment1

p-value2

control1

treatment1

p-value2

control1

treatment1

p-value2

isi

89%

85%

0.206

61%

31%

0.000

55%

29%

0.000

psqi

94%

96%

0.333

85%

70%

0.003

84%

53%

0.000

phq

31%

38%

0.148

32%

19%

0.019

29%

18%

0.041

gad

30%

33%

0.494

26%

17%

0.061

27%

16%

0.052

wsas

74%

72%

0.721

68%

55%

0.041

69%

49%

0.001

1%

2Pearson's Chi-squared test